Channeling elements in an analytics engine environment

ABSTRACT

Embodiments include techniques for channeling elements in an analytics engine environment, techniques include analyzing a current process, and identifying critical data element types associated with the current process. The techniques also include receiving a real-time data stream including a plurality of data elements, and pre-filtering the plurality of data elements, wherein the pre-filtering determines one or more of the plurality of data elements are associated with the critical data element type. The technique includes selecting a channel of a plurality of channels to fast-path the one or more of the plurality of data elements based at least in part on the pre-filtering, and increasing a confidence level corresponding to the current process and the critical data element type based on a state of the current process.

BACKGROUND

The present invention generally relates to routing and forwarding data,and more specifically, to channeling elements in an analytics engineenvironment.

Data analytics are a critical component to monitor and analyze theperformance of a system. Data analytics can be used to troubleshootdifferent processes by analyzing large amounts of information todetermine any patterns and correlations among the data. During atroubleshooting operation, it is important to collect as muchinformation as possible that may be associated with the issue or pointof focus. The further away from the event the information is collected,the relevance of the information can become diminished. The reportsproduced from the analytics engines can be used by system developers andadministrators to modify the configuration of the software and hardwareto improve the performance of their systems and networks.

SUMMARY

Embodiments of the present invention are directed to acomputer-implemented method for channeling data elements in an analyticsengine environment. A non-limiting example of the computer-implementedmethod includes analyzing a current process, and identifying criticaldata element types associated with the current process. The method alsoincludes receiving a real-time data stream including a plurality of dataelements, and pre-filtering the plurality of data elements, wherein thepre-filtering determines one or more of the plurality of data elementsare associated with the critical data element type. The method includesselecting a channel of a plurality of channels to fast-path the one ormore of the plurality of data elements based at least in part on thepre-filtering, and increasing a confidence level corresponding to thecurrent process and the critical data element type based on a state ofthe current process.

Embodiments of the present invention are directed to a system forchanneling data elements in an analytics engine environment. Anon-limiting example of the system includes a receiver, a plurality ofchannels, an analytics engine, and a storage medium, the storage mediumbeing coupled to a processor. The processor is configured to analyze acurrent process, and identify critical data element types associatedwith the current process. The processor is configured to further receivea real-time data stream including a plurality of data elements, andpre-filter the plurality of data elements, wherein the pre-filteringdetermines one or more of the plurality of data elements are associatedwith the critical data element type. The processor is configured toselect a channel of a plurality of channels to fast-path the one or moreof the plurality of data elements based at least in part on thepre-filtering, and increase a confidence level corresponding to thecurrent process and the critical data element type based on a state ofthe current process.

Embodiments of the invention are directed to a computer program productfor channeling data elements in an analytics engine environment, thecomputer program product comprising a computer readable storage mediumhaving program instructions embodied therewith. The program instructionsare executable by a processor to cause the processor to perform amethod. A non-limiting example of the method includes analyzing acurrent process, and identifying critical data element types associatedwith the current process. The method also includes receiving a real-timedata stream including a plurality of data elements, and pre-filteringthe plurality of data elements, wherein the pre-filtering determines oneor more of the plurality of data elements are associated with thecritical data element type. The method includes selecting a channel of aplurality of channels to fast-path the one or more of the plurality ofdata elements based at least in part on the pre-filtering, andincreasing a confidence level corresponding to the current process andthe critical data element type based on a state of the current process.

Additional technical features and benefits are realized through thetechniques of the present invention. Embodiments and aspects of theinvention are described in detail herein and are considered a part ofthe claimed subject matter. For a better understanding, refer to thedetailed description and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The specifics of the exclusive rights described herein are particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe embodiments of the invention are apparent from the followingdetailed description taken in conjunction with the accompanying drawingsin which:

FIG. 1 depicts a cloud computing environment according to one or moreembodiments of the present invention;

FIG. 2 depicts abstraction model layers according to one or moreembodiments of the present invention;

FIG. 3 illustrates a block diagram illustrating one example of aprocessing system for practice of the teachings herein;

FIG. 4 depicts a system for channeling data elements in an analyticsengine environment in accordance with one or more embodiments; and

FIG. 5 depicts a method for channeling data elements in an analyticsengine environment in accordance with one or more embodiments.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagram or the operations described therein withoutdeparting from the spirit of the invention. For instance, the actionscan be performed in a differing order or actions can be added, deletedor modified. Also, the term “coupled” and variations thereof describeshaving a communications path between two elements and does not imply adirect connection between the elements with no interveningelements/connections between them. All of these variations areconsidered a part of the specification.

In the accompanying figures and following detailed description of thedisclosed embodiments, the various elements illustrated in the figuresare provided with two or three digit reference numbers. With minorexceptions, the leftmost digit(s) of each reference number correspond tothe figure in which its element is first illustrated.

DETAILED DESCRIPTION

Various embodiments of the invention are described herein with referenceto the related drawings. Alternative embodiments of the invention can bedevised without departing from the scope of this invention. Variousconnections and positional relationships (e.g., over, below, adjacent,etc.) are set forth between elements in the following description and inthe drawings. These connections and/or positional relationships, unlessspecified otherwise, can be direct or indirect, and the presentinvention is not intended to be limiting in this respect. Accordingly, acoupling of entities can refer to either a direct or an indirectcoupling, and a positional relationship between entities can be a director indirect positional relationship. Moreover, the various tasks andprocess steps described herein can be incorporated into a morecomprehensive procedure or process having additional steps orfunctionality not described in detail herein.

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements but can include other elements not expressly listed or inherentto such composition, mixture, process, method, article, or apparatus.

Additionally, the term “exemplary” is used herein to mean “serving as anexample, instance or illustration.” Any embodiment or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. The terms “at least one”and “one or more” may be understood to include any integer numbergreater than or equal to one, i.e. one, two, three, four, etc. The terms“a plurality” may be understood to include any integer number greaterthan or equal to two, i.e. two, three, four, five, etc. The term“connection” may include both an indirect “connection” and a direct“connection.”

The terms “about,” “substantially,” “approximately,” and variationsthereof, are intended to include the degree of error associated withmeasurement of the particular quantity based upon the equipmentavailable at the time of filing the application. For example, “about”can include a range of ±8% or 5%, or 2% of a given value.

For the sake of brevity, conventional techniques related to making andusing aspects of the invention may or may not be described in detailherein. In particular, various aspects of computing systems and specificcomputer programs to implement the various technical features describedherein are well known. Accordingly, in the interest of brevity, manyconventional implementation details are only mentioned briefly herein orare omitted entirely without providing the well-known system and/orprocess details.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 1, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provides pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and action analytics and notifications 96.

Referring to FIG. 3, there is shown an embodiment of a processing system100 for implementing the teachings herein. In this embodiment, thesystem 100 has one or more central processing units (processors) 101 a,101 b, 101 c, etc. (collectively or generically referred to asprocessor(s) 101). In one or more embodiments, each processor 101 mayinclude a reduced instruction set computer (RISC) microprocessor.Processors 101 are coupled to system memory 114 and various othercomponents via a system bus 113. Read only memory (ROM) 102 is coupledto the system bus 113 and may include a basic input/output system(BIOS), which controls certain basic functions of system 100.

FIG. 3 further depicts an input/output (I/O) adapter 107 and a networkadapter 106 coupled to the system bus 113. I/O adapter 107 may be asmall computer system interface (SCSI) adapter that communicates with ahard disk 103 and/or tape storage drive 105 or any other similarcomponent. I/O adapter 107, hard disk 103, and tape storage device 105are collectively referred to herein as mass storage 104. Operatingsystem 120 for execution on the processing system 100 may be stored inmass storage 104. A network adapter 106 interconnects bus 113 with anoutside network 116 enabling data processing system 100 to communicatewith other such systems. A screen (e.g., a display monitor) 115 isconnected to system bus 113 by display adaptor 112, which may include agraphics adapter to improve the performance of graphics intensiveapplications and a video controller. In one embodiment, adapters 107,106, and 112 may be connected to one or more I/O busses that areconnected to system bus 113 via an intermediate bus bridge (not shown).Suitable I/O buses for connecting peripheral devices such as hard diskcontrollers, network adapters, and graphics adapters typically includecommon protocols, such as the Peripheral Component Interconnect (PCI).Additional input/output devices are shown as connected to system bus 113via user interface adapter 108 and display adapter 112. A keyboard 109,mouse 110, and speaker 111 all interconnected to bus 113 via userinterface adapter 108, which may include, for example, a Super I/O chipintegrating multiple device adapters into a single integrated circuit.

In exemplary embodiments, the processing system 100 includes a graphicsprocessing unit 130. Graphics processing unit 130 is a specializedelectronic circuit designed to manipulate and alter memory to acceleratethe creation of images in a frame buffer intended for output to adisplay. In general, graphics processing unit 130 is very efficient atmanipulating computer graphics and image processing and has a highlyparallel structure that makes it more effective than general-purposeCPUs for algorithms where processing of large blocks of data is done inparallel.

Thus, as configured in FIG. 3, the system 100 includes processingcapability in the form of processors 101, storage capability includingsystem memory 114 and mass storage 104, input means such as keyboard 109and mouse 110, and output capability including speaker 111 and display115. In one embodiment, a portion of system memory 114 and mass storage104 collectively store an operating system coordinate the functions ofthe various components shown in FIG. 3.

As analytics engines process large amounts of data, it becomes importantto ensure that when a particular process is being analyzed, the mostrelevant information related to the process is received in a timelymanner. If the data is not received in time, the data that was oncevaluable is now obsolete and cannot be used to solve the problem. Theanalytics engine may not receive the data in time because of an overloadin received data, network delays, and lag time in processing. Ifobsolete data is sent over a priority path, this can prevent other dataelements that are actually critically to solving the issue from beingreceived in time. The analytics engine can experience a delay inprocessing the critical elements that are needed because thenon-critical information has not been filtered based on the urgency ofthe targeted process.

Turning now to an overview of the aspects of the invention, one or moreembodiments of the invention address the above-described shortcomings ofthe prior art by providing a technique to identify critical elements ofa real-time data stream that are needed for solving a current problembeing analyzed by the analytics engine. The analytics engine will relaythe critical data element types that are required for solving and/oranalyzing the existing issue to a receiver of the data stream. Thereceiver that receives the information will identify the criticalelements of the data stream and determine a channel to fast path thecritical data to the analytics engine for processing.

The described technique considers several factors when determining thedata elements and the channel to fast path through the system from thereceived to an analytics engine. First, a process for analysis and theassociated critical elements are identified to resolve the issues aredetermined. The identified problem will be correlated to the incomingdata stream to determine which data of the received real-time stream arecritical elements. Subsequently, the analytics engine will notify thereceiver to pre-filter any received data including the criticalelements.

Next, the critical data elements will be pre-filtered from the incomingdata stream. After the pre-filtering is performed, one of the availablechannels in the system will be selected to fast path the data to ensurethe data is received at the analytics engine in an efficient manner. Thespeed of each channel will be determined and the congestion that isassociated with each channel will also be determined when making theselection. As the data is received at the analytics engine, theanalytics can determine whether the critical elements of the data isassisting in troubleshooting the issue and if so, the confidence levelfor correlating the issue and the critical elements can be increased. Adatabase correlating the issue, critical elements, and confidence levelcan be stored to subsequently address the issue or similar issue in suchan event.

The above-described aspects of the invention address the shortcomings ofthe prior art by fast pathing elements that have been determined to becritical to solving a current issue.

Referring now to FIG. 4, a system 400 for channeling data elements in ananalytics engine environment is provided. The system 400 includes areceiver 402. The receiver 402 is configured to receive real-time datastreams from one or more different sources. In addition, the receiveddata can arrive in different formats, protocols, and at different rates.The receiver 402 is configured to pre-filter the individual dataelements of the real-time data stream and select a channel 404 to fastpath the individual data elements to the analytics engine 406. Thechannel 404 selection can be based on several factors including thereceived data type and relevance to the problem being analyzed by theanalytics engine, the channel configuration and availability of eachchannel, the feedback from the analytics engine, etc.

The channels 404 can comprise different bandwidths and performance. Forexample, the channel 404 a can be configured as a low speed channel, 404b and 404 c can be configured as medium speed channels, and channel 404d can be configured as a high speed channel. In an embodiment, a channelis a HiperSocket for a system Z and is configured with the fastestspeeds. A HiperSocket is an IBM technology for high-speed communicationsbetween partitions on a server with a hypervisor.

In the non-limiting example provided in FIG. 4, the critical dataelement C is forwarded over the high speed channel 404 d, while theother data elements D are transmitted over the other channels.

In one or more embodiments, the system 400 can be configured with adifferent number of channels having different performancecharacteristics. In the system 400, the plurality of channels 404 arecoupled to the analytics engine 406 which processes the data. Theanalytics engine 406 can analyze big data from storage 408 including aplurality of databases 410. The analytics engine 406 is configured todetermine any patterns or correlations of data that associated with aparticular process or issue.

The analytics engine 406 can store records in the storage 408 of thelearned patterns relating the data element types to the specific issuesor target processes being analyzed. In addition, the analytics engine406 can set and update a confidence level based on the success orresolving issues based on the information provided in the records. Inone or more embodiments, the system can be coupled to a cloud networkfor the exchange of data and communication.

The analytics engine 406 is also configured to provide feedback 412 to areceiver 402 to update the critical element type that is associated withthe current process being analyzed. The receiver 402 uses the feedback412 to dynamically update and fast path the received critical dataelements from the real-time data stream to the analytics engine 406. Inone or more embodiments, more than one analytics engine 406 can existwithin the system.

In a non-limiting example, in a healthcare setting a patient maydescribe a condition to a doctor indicating signs of blurry vision,constant hunger and thirst, persistent fatigue, and tingling feet. Dataincluding the patient's symptoms, medical history, and personalinformation is collected. This information can be entered into ananalytics engine along with data from other sources. As data is entered,over time the system learns to recognize certain data elements thatprovide additional value if input data shows signs of diabetes duringanalysis. In this particular example, certain data elements, such asblood sugar level, numb hands, or excessive thirst are recognized by thesystem as providing more immediate value than other data elements suchas weight, family history, or fatigue level. These data elements can befast pathed so they can be ingested into the system quicker and lead toan increased confidence level of diagnosis.

Referring now to FIG. 5, a method 500 for channeling data elements in ananalytics engine environment is provided. Block 502 provides analyzing acurrent process. In one or more embodiments, an analytics engine istroubleshooting an issue associated with a particular process. In one ormore embodiments, the current process includes analyzing data receivedfrom storage and the real-time data received from the receiver.

Block 504 provides identifying critical data element types associatedwith the current process. In one or more embodiments, a plurality ofcritical data element types can be determined. As a non-limitingexample, a critical data element type can be based on a particularsource of data, format, temporal information, etc. For example, thecritical data elements can indicate a source and data elements that werereceived in the last five minutes. If both criteria are not met, thenthe particular data element does not qualify as critical element data tobe fast pathed over one or more channels in the system.

Block 506 provides receiving a real-time data stream including aplurality of data elements. In one or more embodiments, a received isconfigured to receive real-time data from a plurality of sources. Thereal-time data includes a plurality of data elements of different types.In an embodiment, information indicating the type of data elements isprovided in the metadata associated with each data element. The receivercan be configured to read the metadata and pre-filter each data elementof the real-time data stream based on the metadata.

Block 508 provides pre-filtering the plurality of data elements, whereinthe pre-filtering determines one or more of the plurality of dataelements are associated with the critical data element type. In one ormore embodiments, the pre-filtering is performed by a receiver of thereal-time data stream. In one or more embodiments, the receiver executesthe pre-filtering operation based on the associated metadata for eachdata element.

Block 510 provides selecting a channel of a plurality of channels tofast-path the one or more of the plurality of data elements based atleast in part on the pre-filtering. In one or more embodiments, thereceiver selects the channel based on the bandwidth of each of thechannels and/or also the individual states of each of the channels. Inan embodiment, the receiver can be configured with the bandwidthinformation for each channel. In a scenario, where a channel having thehighest bandwidth is congested, a different channel can be selected totransmit the data elements to the analytics engine. The objective of thereceiver is to select the channel to transmit the critical data elementsthe fastest to the analytics engine in an efficient manner, providing attimes the channel configured with the highest bandwidth is not selected.

In one or more embodiments, the data channels are defined with a datatravel/priority speed. In one embodiment, in the system Z a channel canbe a HiperSocket which can forward the tagged data over the selectedchannel. In one or more embodiments, the receiver can receive back fromthe analytics engine to determine the type of data elements that arecritical to resolving the issue currently being processed by theanalytics engine. In an example, in a particular instance a temporalthreshold for data can be set to four hours, however a shift in theanalysis of the current process may require a 30 minute temporalthreshold. This indication be updated and fed back to the receiver forpre-filtering. Other criteria can be provided to the receiver topre-filter the data elements for fast-pathing. In one or moreembodiments, the data type and other metadata can be matched todetermine which the data elements to be fast pathed. For example, if thedata is relative but too old, the information will not be fast pathed.The age of the incoming data is important in the analysis of the currentprocess.

In one or more embodiments, the fast pathing includes tagging, by thereceiver, the pre-filtered data elements to indicate the critical dataelement type for fast pathing over the one of a plurality of channelsand using the tags to transmit the data over a selected fast pathchannel. In one or more embodiments, the urgent data is tagged for fastpathing over a channel.

Block 512 provides increasing a confidence level corresponding to thecurrent process and critical data element type based on the state of thecurrent process. In one or more embodiments, the system can storeconfidence level information indicating the critical data elementtype(s) that aided in resolving the current process being studied by theanalytics engine.

In one or more embodiments, the analytics engine can detect patternsamong the various processes and identify critical elements and use thatinformation for fast pathing data elements. The channeling of the dataelements can be based on the self-learned information.

In one or more embodiments, the described techniques are an improvementof the prior art by providing a mechanism to fast path critical elementsof a real-time data stream based at least in part on a current problembeing targeted by an analytics engine. Because the issues that aretargeted by the analytics engine can change over time, the type ofcritical elements used to analyze each issue can also vary over time.For example, critical elements in one instance may be valuable while inanother instance all criticality is lost based on when the informationis received. This information and the status and configuration of theplurality of channels are used to select a channel to deliver thecritical data elements to the analytics engine efficiently. This systemdoes not simply select the channel having the highest transfer rate butalso considers the availability of each channel. A channel that isconfigured with the highest transfer rate may not be selected if thechannel is congested with a plurality of data elements.

The techniques described herein can increase the efficiency of acomputing system by resolving system issues by fast pathing the urgentdata that are critical to the analysis. Additionally, if the issues areresolved in a timely manner the bandwidth usage of the channels in thesystem can be managed to increase performance of the system by balancingthe data elements over the available channels in the system. Also,information can be provided to the systems and/or external systems thatare being analyzed by the analytics engine. The system is able toself-learn the optimal combinations of critical data element types andassociated targeted processes by using confidence levels for eachcombination. The system increases the issue resolution efficiency in thesystem.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instruction by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdescribed herein.

1.-7. (canceled)
 8. A system for channeling elements in an analyticsengine environment, the system comprising: a receiver including adecision engine; a plurality of channels; an analytics engine; a storagemedium, the storage medium being coupled to a processor; the processorconfigured to: analyze a current process; identify critical data elementtypes associated with the current process; receive a real-time datastream including a plurality of data elements; pre-filter the pluralityof data elements, wherein the pre-filtering determines one or more ofthe plurality of data elements are associated with the critical dataelement type; select a channel of a plurality of channels to fast-paththe one or more of the plurality of data elements based at least in parton the pre-filtering; and increase a confidence level corresponding tothe current process and the critical data element type based on a stateof the current process.
 9. The system of claim 8, wherein the processoris further configured to provide feedback to update the critical elementtype to be pre-filtered based on the analyzing of the current process,wherein the pre-filtering is based on metadata associated with each ofthe one or more of the plurality of data elements.
 10. The system ofclaim 8, wherein the processor is further configured to store a recordcorrelating the current process and critical data element type, andincreasing the confidence level based on a performance of the currentprocess.
 11. The system of claim 8, wherein the selection includestagging the pre-filtered one or more of the plurality of data elementsto indicate the critical data element type for fast pathing over theselected channel of the plurality of channels.
 12. The system of claim8, wherein the processor is further configured to define a confidencelevel threshold corresponding to the current process and the criticalelement data type.
 13. The system of claim 8, wherein the processor isfurther configured to define a temporal threshold for the criticalelement data type based on the current process.
 14. The system of claim8, wherein the selected channel is based at least in part on a bandwidthand current availability each of the plurality of channels.
 15. Acomputer program product for channeling elements in an analytics engineenvironment, the computer program product comprising: a computerreadable storage medium having stored thereon first program instructionsexecutable by a processor to cause the processor to: analyze a currentprocess; identify critical data element types associated with thecurrent process; receive a real-time data stream including a pluralityof data elements; pre-filter the plurality of data elements, wherein thepre-filtering determines one or more of the plurality of data elementsare associated with the critical data element type; select a channel ofa plurality of channels to fast-path the one or more of the plurality ofdata elements based at least in part on the pre-filtering; and increasea confidence level corresponding to the current process and the criticaldata element type based on a state of the current process.
 16. Thecomputer program product of claim 15, wherein the instructions arefurther executable by the processor to cause the processor to providefeedback to update the critical element type to be pre-filtered based onthe analyzing of the current process, wherein the pre-filtering is basedon metadata associated with each of the one or more of the plurality ofdata elements.
 17. The computer program product of claim 15, wherein theinstructions are further executable by the processor to cause theprocessor to store a record correlating the current process and criticaldata element type, and increasing the confidence level based on aperformance of the current process.
 18. The computer program product ofclaim 15, wherein the selection includes tagging the pre-filtered one ormore of the plurality of data elements to indicate the critical dataelement type for fast pathing over the selected channel of the pluralityof channels.
 19. The computer program product of claim 15, wherein theinstructions are further executable by the processor to cause theprocessor to define at least one of a confidence level thresholdcorresponding to the current process and the critical element data typeor a temporal threshold for the critical element data type based on thecurrent process based on the current process.
 20. The computer programproduct of claim 15, wherein the selected channel is based at least inpart on a bandwidth and current availability each of the plurality ofchannels.